CONTROL OF RNA FUNCTION BY CONFORMATIONAL DESIGN CYCLING IN ENERGY - - PowerPoint PPT Presentation

1

March 1 , 2016

CONTROL OF RNA FUNCTION BY CONFORMATIONAL DESIGN

CYCLING IN ENERGY AND DESIGN LANDSCAPES Stefan Badelt Department of Theoretical Chemistry Theoretical Biochemistry Group (tbi) University of Vienna

st

2

OUTLINE

RNA modeling and RNA design Design of RNAs with prion-like behavior Design of self-processing ribozymes

Kinetics of RNA-RNA interactions

3

RNA STRUCTURE

GCGGAUUUAGCUCAGUUGGGAGAGCGCCAGACUGAAGAUCUGGAGGUCCUGUGUUCGAUCCACAGAAUUCGCACCA

A secondary structure is a list of base pairs, where: A base may participate in at most one base pair Base pairs must not cross (no pseudoknots) Only isosteric base-pairs (GC, AU, GU) are allowed.

4

THE NEAREST NEIGHBOR ENERGY MODEL

H: Hairpin loop I: Interior loop M: Multi loop E: Exterior loop H H M I I I I I

5' 3'

E I I

A C G G G C U G A C U U A A U U G U C G A G G A A A C C A U C U G C G C A U 10 20 30

5' 3'

E(s) = e(l) ∑

l∈s

5

ENERGY LANDSCAPES

An energy landscape is defined by Conformation space Neighborhood relation [Move set] Energy function

s ∈ Ω M(s) E(s)

6

ENERGY LANDSCAPES

free energy [kcal/mol]

0 kcal/mol

folding pathways equilibrium partition function

Z

UGCGACGUCCGACCUCGUUUACGCCAGUACCCCACUUCUCUUUG

minimum free energy structure prediction suboptimal structure prediction MFE

Z = ∑s∈Ω e

−E(s) kT

G = −kT ln Z P(s) = e−E(s)/kT

Z

7

COMPUTATIONAL RNA DESIGN

FROM STRUCTURE(S) TO SEQUENCE

10 20 30 40 50 60 70 1

GCGGAUUUAGC UCAGUUGGGAG AGCGCCAGACU GAAGAUCUGGA GGUCCUGUGUU CGAUCCACAGA AUUCGCACCA

Φ(σ)

The objective function can vary

structure is MFE of sequence, maximize probability of structure, ...

Φ(σ)

8

... simple adaptive walks usally lead to satisfactory solutions RNA DESIGN IS FORMALLY HARD, BUT EASY IN PRACTICE

sequences minimum free energy structures mapping redundant, sensitive (common motifs realized more often) (bigger)

9

DEPENDENCY GRAPHS FOR BISTABLE RNA DESIGN

(((...)))((...))((...)). .(((.((.(((...))).)).)))

• C. Flamm, I.L. Hofacker, S. Maurer-Stroh, P.F. Stadler, and M. Zehl.

Design of multi-stable RNA molecules. RNA, 7:254–265, 2001

10

ON THE FUNCTION OF RIBOSWITCHES

riboswitch protein coding region promoter region Terminator OFF

Ligand

riboswitch protein coding region promoter region Anti- Terminator ON + Ligand

• Ligand

11

CAN WE DEMONSTRATE AUTOCATALTYIC COFORMATIONAL SELF-REPLICATION?

12

THE MECHANISM OF A PRION

N C

PrPC PrPScoligomer PrPScoligomer

N C N

+

PrP+

N C

PrPC PrPSc

N

PrPSc

N

PrPSc

N N C N

PrPC-PrPSc heterodimer

N N

PrPSc-PrPSc dimer PrPC PrPSc PrPScprotofibril

Aguzzi, A., Sigurdson, C., and Heikenwaelder, M. (2008). Molecular mechanisms of prion

• pathogenesis. Annual Review of Pathology: Mechanisms of Disease, 3, 11–40.

13

S1 S2 normal infectious

free energy [kcal/mol]

MFE maximize refolding barrier

Requirements for an RNA prion S1 S2 Energy Landscape ΔG‡

S2 S1

ΔG‡

+

HIV Dis type kissing loop complex

free energy [kcal/mol]

MFE minimize refolding barrier

S1 S2 S2 S2 S1 S2 S2 S2

+

S1 S2 S2 S2

S1 S2 S2 S2

14

PRION DESIGN PIPELINE

[switch.pl]

....(((((((..((((...(((((...)))))...))))..))))))) (((((((.........)))))))....((((((.........)))))). NNNNNNNAACCGACGANNNNNNNNNNNNNNNNNAACGUCGGANNNNNNN

thermodynamic candidate molecules [RNAsubopt + barriers] (M) [findpath] (H) [RNAsubopt + barriers] (D) final ranking of molecules

15

E(S1) + E(S2) E(S1 S2) E(S2 S2)

S1+S2 S1 S2

free energy [kcal/mol]

S1 S2 S2 S2

ΔG‡ ΔG‡

16

ZM

ZS1 ZS2 Zc1 Zc2 Zdup

[S1] = [M] + ( +

) [D]

ZS1 ZM ZS1+c1 Zc1 ZS1+c2 Zc2

17

FOLDING BARRIERS WITH AND WITHOUT AUTO-CATALYSIS

20 40 60 80

• 20
• 25
• 30
• 35

S1 + S2

• 23.40 kcal/mol
• 33.60 kcal/mol
• 17.00 kcal/mol
• 16.10 kcal/mol
• 29.70 kcal/mol

S2 + S2 + kiss

length of refolding path [base-pair moves] free energy [kcal/mol]

20 40 60 80

5

• 5
• 10
• 10.70 kcal/mol

S2

• 12.70 kcal/mol

S1

6.00 kcal/mol

S2 => S1: 16.70 kcal/mol S1 => S2: 13.60 kcal/mol S1 => S2: 9.80 kcal/mol

• 31.80 kcal/mol
• 22.00 kcal/mol

Energy Model 2 Energy Model 1

• 39.00 kcal/mol

S. Badelt, C. Flamm, and I.L. Hofacker. Computational design of a circular RNA with prionlike behavior. Artificial Life 22, pages 1–14, 2016

18

CAN AN RNA CIRCULARIZE ITSELF?

O B1 O O O H O B2 O O H P O

• O

O O B1 O O O O B2 O O H P O O- O- O B1 O O O O B2 O O H P O O O- :B -

+A-H

H H B A

δ− δ+

H A: H B R1 R1 R2 R2 R1 R2

cleavage ligation

19

DESIGN OF SELF-PROCESSING RNA

LCR L + CR L + C + R LCR LC + R L + O + R

20

Biochemistry Physics Computational Biology

Greifswald: Sabine Müller Sonja Petkovic Wien: Ivo Hofacker Christoph Flamm Stefan Badelt Greifswald/Göteborg: Mihaela Delcea Stephan Block

• 27.20

• 26.40 (8.5)
• 29.20 (2.8)
• 29.50
• 23.30
• 27.60 (3.1)
• 23.00 (7.2)

• 23.20 (2.8)
• 29.80
• 31.40
• 26.70

• 23.80

• 27.50

• 31.10 (4.2)

• 28.00

• 34.00 (1.6)
• 33.90 (1.3)
• 34.00
• 34.10
• 31.60 (1,4)
• 34.00 (1.3)

• 27.40 (1.3)
• 27.80
• 34.00
• 34.10

• 28.00

• 27.80

• 33.90 (1.9)

21 LCR L + CR L + C + R LCR LC + R L + O + R

compute a set of candidate molecules (switch.pl) maximize probabilities to form reactive conformations

• ptimize towards reactive monomers and/or dimers

***** * * * ****** ******* ********* ***** 1.......10........20........30........40........50........60........70........80........90.......100........

• --...((((....(((((((((((....)))))))))))....))))(((((.......(((....))).........)))))........................
• --.................(((((....)))))((((((....(((((((((.......(((....))).........))))).....))))....)))))))....

CRZ2 PBD1 PBD2 PBD3 PBD4

GGGAGAUCACAGUCCUCUUUGACGGGGUUCCGUCAAAGAGAGAAGUGAACCAGAGAAACACACUUCGGUGGUAUAUUACCUGGUCCCCCUCACAGUCCUCUUU---- GGGAGAGCACAGUCGGAGUUGCCGCGUUAGCGGCGGUUCUAGAAGUGCCCCGCAGAAACAGCCAUAUGGCGUAUAUUACGCGGGAAAAAGCACAGUCGGAACC---- GGGAGAGAACAGUCGGUGGUGCCCCGUAAGGGGCGUCGCCAGAAGUUCGGACCAGAAACAGCCAAAAGGCGUAUAUUACGGUCCAAAAAGAACAGUCGGCGAC---- CAGUCCGGUUUACCGCUAAUGCGGUGGGUCGAGAAGUCUGAGCGAGAAACACAGUAUACUGGUAUAUUACCGCUCCAUAAAGGCAGUCCGGCACCAAA CAGUCCGGUUUACCGCUAAUGCGGUGGGUCGAGAAGUCUGAGCGAGAAACACAGGACACUGGUAUAUUACCGCUCCAUAAAGGCAGUCCGGCACCAAA

• GGGAGA

GGGAGA

22

2D 3D AFM

23

DISSOCIATION BARRIERS DETERMINE EFFICIENCY CRZ-2

1033' 1035' 94•3' 5'•92 923' 945' 83•3' 5'•83 83c c83l 5' 3' 5' 92 94

• 27.20
• 26.40 (8.5)
• 29.20 (2.8)
• 29.50
• 23.30
• 27.60 (3.1)
• 23.00 (7.2)

4.5

• 23.20 (2.8)
• 29.80
• 31.40
• 26.70

3' 83

3.6

• 23.80

0.0 3.6 6.2

83

• 27.50

5.7 5.0 9.7 4.2 8.9

• 31.10 (4.2)

(2.7)

dissociation barrier free energy (activation energy) refolding barrier

24

RESULTS RNA (in theory) capable of conformational self-replication ribozymes can be designed to ligate themselves energy barriers for dissociation are important for design experimental data to model interactions of ribozymes

25

SUMMARY

Output >Input AGGAACAGUCGCACU ACCCACCUCGACAUC GUAAAUCAAAUUGGA ACUGAAGCCCUUGGU CUGGAGUCACCAGGG GGUUUACGUACUACU modeling modeling design design initial state final state Reality experimental setup final observation Design landscapes

• bjective function

AGCAACA AGGAUCU AGGAUCA AGGAACA Input design >Output AGGAACAGUCGCACU ACCCACCUCGACAUC GUAAAUCAAAUUGGA ACUGAAGCCCUUGGU CUGGAGUCACCAGGG GGUUUACGUACUACU modeling Energy landscapes free energy

• 18.0
• 16.0
• 14.0
• 12.0
• 10.0
• 8.0

26

THANKS TO

Advisers: Ivo L. Hofacker, Christoph Flamm PhD Committee: Sabine Müller, Peter F. Stadler ViennaRNA package: Ronny Lorenz self-processing RNA: Sonja Petkovic, Stephan Block cotranscriptional folding/design: Peter Kerpedjiev, Stefan Hammer, Andrea Tanzer, Michael T. Wolfinger, Michael Jantsch, Konstantin Licht, Mansoureh Tajaddod RNA design: Mario Mörl, Regula Arreger

This research was funded in parts by the FWF International Programme I670, the DK RNA program FG748004 and the FWF project "SFB F43 RNA regulation of the transcriptome".

27

THE AWESOME TBI

28

RNA-RNA INTERACTIONS

toehold interactions

29

G C

refolding with or without a toehold Hulk design

A C U G G G G G G G G G U U U A A A A A A U U G C C C C A C C U U A A U U U U U A A C C C

30

10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 10 1 10 2 10 3 10 4 10 5 10 6

tim e [se conds]

0.0 0.2 0.4 0.6 0.8 1.0

• ccupa ncy [m ol/l]

1e 9 Hulk de sign: 1nM switch-RNA 1m M trigge r-RNA

C U G A G G U G G G G U A G G A U A A G A U U G C C C C A C A

C U G A G G U G G G G U A G G A U A A G A U U G C C C C A C A C C U U A U U C U A C C U U A A

31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

• 18.0
• 16.0
• 14.0
• 12.0
• 10.0
• 8.0

0.7 1.4 3.1 3.1 0.8 0.8 0.8 0.8 2.6 2.6 3.5 2.2 0.8 2.4 3.5 1.2 0.8 0.6 0.8 0.8 1.0 1.3 0.7 1.0 1.3 0.799999 1.2 1.2 0.8 0.9 0.8 0.8 1.2 1.2 2.6 0.8 1.4 1.7 0.8 0.8 0.7 1.2 1.1 0.7 0.8 0.6 0.8 1.5 1.0 3.0 3.5 3.1 0.8 1.9 1.3 1.3 0.8 0.7 1.0 1.3 0.8 1.6 1.5 4.2 0.8 1.9 2.0 1.9 1.4 1.2 1.0 0.599999 2.0 2.3 2.6 1.5 1.3 2.6 2.3 2.9 2.0 2.6 0.7 1.3 2.42.8 1.2 1.2 0.8 2.3 1.3 1.1 2.3 1.2 2.6 2.6 3.0 2.8 2.3 4.4 4.2 3.9 2.8 3.5 0.7 0.7 3.7 3.9

32 . 1

ENERGY LANDSCAPES

Free energy

Christoph Flamm, Walter Fontana, Ivo L Hofacker, and Peter Schuster RNA folding at elementary step resolution. RNA, 6:325–338, 2000. Michael T. Wolfinger, Andreas Svrcek-Seiler, Christoph Flamm, Ivo L. Hofacker, and Peter F. Stadler. Efficient computation of RNA folding dynamics. Journal of Physics A: Mathematical and General, 37:4731–4741, 2004.

32 . 2

... where is a constant to relate folding to wall-clock time KINETICS Calculate transition rates from energy barriers

Δ = E( ) − E( ) G‡ sj si = { kij k0 k0e− ΔG‡

RT

if Δ ≤ 0 G‡

• therwise

k0

• N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller. Equation of state

calculations by fast computing machines. The Journal of Chemical Physics, 21(6):1087–1092, 1953.

32 . 3

• 26.0
• 24.0
• 22.0
• 20.0
• 18.0
• 16.0
• 14.0
• 12.0

= P(i|α) kαβ ∑

i∈α ∑ j∈β

kij

THE MASTER EQUATION

= ( (t) − (t) ) d (t) Pi dt ∑

i≠j

Pj kji Pi kij

... together with the rate of gradient basin transitions ...

32 . 4

• 10.0
• 8.0
• 6.0
• 4.0
• 2.0

... can be solved for 60-80 nucleotides sequence length